Wavelet Deconvolution in a Periodic Setting with Long-Range Dependent Errors

In this paper, a hard thresholding wavelet estimator is constructed for a deconvolution model in a periodic setting that has long-range dependent noise. The estimation paradigm is based on a maxiset method that attains a near optimal rate of convergence for a variety of L_p loss functions and a wide variety of Besov spaces in the presence of strong dependence. The effect of long-range dependence is detrimental to the rate of convergence. The method is implemented using a modification of the WaveD-package in R and an extensive numerical study is conducted. The numerical study supplements the theoretical results and compares the LRD estimator with na\"ively using the standard WaveD approach

The Influence of Occupational Driver Stress on Work-related Road Safety: An Exploratory Review

Research has identified a number of stressors that could impact on the occupational driver by increasing stress levels and, for some individuals, causing adverse behaviour and effects, for example, aggressive behaviour, fatigue, inattention/distraction, and substance abuse. For safety professionals and employers, one way to reduce the effects of occupational driver stress is to change perceptions so that management and drivers recognise that work-related driving is as important as other work-related tasks. This article explores relevant literature in relation to driver stress and suggests additions to risk management processes and safety procedures/policies, including assigning sufficient basic resources to target occupational stress (particularly occupational driver stress)

The preparation of ketene dithioacetals and thiophenes from chloropyridines containing an active methylene group

The base catalysed reaction of carbon disulphide with the active methylene groups of chloropyridines 4 and 7, followed by alkylation with reagents which also contain active methylene groups, lead to ketene dithioacetals. Further reaction with base afforded highly substituted thiophenes

Kink estimation in stochastic regression with dependent errors and predictors

In this article we study the estimation of the location of jump points in the first derivative (referred to as kinks) of a regression function \mu in two random design models with different long-range dependent (LRD) structures. The method is based on the zero-crossing technique and makes use of high-order kernels. The rate of convergence of the estimator is contingent on the level of dependence and the smoothness of the regression function \mu. In one of the models, the convergence rate is the same as the minimax rate for kink estimation in the fixed design scenario with i.i.d. errors which suggests that the method is optimal in the minimax sense.Comment: 35 page